Abstract: Techniques disclosed herein relate to glucose management recommendations based on nutritional information. In some embodiments, the techniques may involve obtaining user input that includes textual input indicating quantitative information for a food item. The techniques may also involve determining, based on the quantitative information for the food item, nutritional information for the food item, where the quantitative information for the food item is different from the nutritional information for the food item. The techniques may further involve generating, based on the nutritional information, a glucose management recommendation.

Abstract: Disclosed herein are techniques related to insulin delivery recommendations based on nutritional information. In some embodiments, the techniques may involve obtaining user input indicative of quantitative information for a food item. The techniques may further involve determining, based on the user input, nutritional information for the food item. Additionally, the techniques may involve generating, based on the nutritional information, an insulin delivery recommendation.

Abstract: An algorithm and method to provide personal recommendations for nutrition based on preferences, habits, medical and activity profiles for users, and constraints. The algorithm can also be fed and takes into account real-time feedback from the user. The method allows creating a personal nutritional schedule based on a set of constraints, which are solved using an optimization algorithm to find the diet best fitting each user. The method also includes analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insightful information and allowing improved performance by means of “machine-learning,” and creating a list of recommended food items/recipes to help users live a balanced, healthier lifestyle.

Abstract: Device, systems, and techniques for supporting a patient's diabetes management with food item recommendations are described in this disclosure. The device, systems, and techniques may be configured to execute a training process for a model to predict a patient nutrition state of a patient based on a predetermined food item consumed by the patient within a time period. The training process is further configured to determine an estimated biomarker level based on the predetermined food item profile having a set of nutritional attributes for the food item and the model; receive an actual biomarker level of the patient after the patient consumes the food item within the time period; and calibrate the model based on comparing the estimated biomarker level to the actual biomarker level; repeat the training process for one or more food items of a set of predetermined food items; and output the trained model.

Abstract: Patient monitoring systems and related devices and operating methods are provided. One exemplary method involves obtaining a predictive association model determined based on historical data associated with the patient that includes a plurality of associations, obtaining real-time data associated with the patient from a plurality of different data sources, determining a current state of the patient based at least in part on the real-time data, predicting occurrence of an event when the current state of the patient corresponds to an association of the plurality of associations, and in response to predicting the occurrence of the event, initiating an action in response to the predicted event.

Abstract: A method, system and platform are provided that can utilize a serverless architecture with autonomous functions to standardize nutritional and health data from various sources into a structured file format suitable for analysis. The platform may include an authentication component, a data retrieving component, a pipeline component, a standardization component, and a storage component. The components may include sets of autonomous functions, streaming applications, notification messages, and other objects logically connected to one another. The components may be connected serially and data may flow through the components sequentially in a stream. Using the disclosed architecture, the platform can aggregate and process large volumes of data in an efficient and cost-effective manner, analyze the standardized structured data, and generate personalized dietary and health advice or recommendations to individual end users.

Abstract: The present disclosure provides methods and systems for providing personalized food and health management recommendations. The method may comprise mapping foods by abstracting information from data relating to foods to develop a food ontology. The method may comprise collecting and aggregating a plurality of data sets related to food, health, or nutritional information of a user. The plurality of data sets may be provided from a plurality of sources in a two or more data formats. The method may comprise converting the plurality of data sets into a standardized format that may be individualized for the user. The method may comprise applying a predictive model to the food ontology and the plurality of data sets of the user in the standardized format to determine effects of food consumption of the user's body.

Abstract: The present disclosure provides methods and systems for providing personalized food and health management recommendations. A dietary glucose monitor may comprise a food analysis module in communication with a glucose level monitor, where the food analysis module may be configured to (1) analyze data indicative of foods consumed by a user, and (2) determine an effect of individual foods on a glucose level of the user, based on changes to the user's glucose level as measured by the glucose level monitor.

Abstract: An algorithm and method to provide personal recommendations for nutrition based on preferences, habits, medical and activity profiles for users, and constraints. The algorithm can also be fed and takes into account real-time feedback from the user. The method allows creating a personal nutritional schedule based on a set of constraints, which are solved using an optimization algorithm to find the diet best fitting each user. The method also includes analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insightful information and allowing improved performance by means of “machine-learning,” and creating a list of recommended food items/recipes to help users live a balanced, healthier lifestyle.

Abstract: An algorithm and method to provide personal recommendations for nutrition based on preferences, habits, medical and activity profiles for users, and constraints. The algorithm can also be fed and takes into account real-time feedback from the user. The method allows creating a personal nutritional schedule based on a set of constraints, which are solved using an optimization algorithm to find the diet best fitting each user. The method also includes analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insightful information and allowing improved performance by means of “machine-learning,” and creating a list of recommended food items/recipes to help users live a balanced, healthier lifestyle.